{"id":25019657,"url":"https://github.com/djeada/stanford-machine-learning","last_synced_at":"2025-04-13T04:11:18.804Z","repository":{"id":63103963,"uuid":"355002544","full_name":"djeada/Stanford-Machine-Learning","owner":"djeada","description":"Welcome to my collection of notes for the Stanford Machine Learning course, led by Professor Andrew Ng. These notes are a compilation of insights, key takeaways, and important concepts I gathered while studying the course material.","archived":false,"fork":false,"pushed_at":"2024-08-28T13:46:47.000Z","size":40153,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-26T21:11:13.943Z","etag":null,"topics":["andrew-ng","coursera","machine-learning"],"latest_commit_sha":null,"homepage":"https://adamdjellouli.com/pages/stanford_machine_learning","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/djeada.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-04-05T23:44:54.000Z","updated_at":"2024-08-28T13:46:55.000Z","dependencies_parsed_at":"2025-02-05T11:51:27.851Z","dependency_job_id":"81db644a-7371-4c18-b593-5b762691874f","html_url":"https://github.com/djeada/Stanford-Machine-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStanford-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStanford-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStanford-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStanford-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/djeada","download_url":"https://codeload.github.com/djeada/Stanford-Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248661704,"owners_count":21141450,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["andrew-ng","coursera","machine-learning"],"created_at":"2025-02-05T11:51:21.754Z","updated_at":"2025-04-13T04:11:18.779Z","avatar_url":"https://github.com/djeada.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/stargazers\"\u003e\u003cimg alt=\"GitHub stars\" src=\"https://img.shields.io/github/stars/djeada/Stanford-Machine-Learning\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/network\"\u003e\u003cimg alt=\"GitHub forks\" src=\"https://img.shields.io/github/forks/djeada/Stanford-Machine-Learning\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/master/LICENSE\"\u003e\u003cimg alt=\"GitHub license\" src=\"https://img.shields.io/github/license/djeada/Stanford-Machine-Learning\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n# Stanford-Machine-Learning\nWelcome to my collection of notes for the Stanford Machine Learning course, led by Professor Andrew Ng. These notes are a compilation of insights, key takeaways, and important concepts I gathered while studying the course material.\n\nWhile this repository is not the official course repository, it serves as a supplement to the course for those looking to review or reinforce their understanding. For full access to the course's lectures, slides, and more, be sure to visit the [Coursera course page](https://www.coursera.org/learn/machine-learning). Feel free to explore these notes as you embark on your own machine learning journey!\n\n![Capture](https://user-images.githubusercontent.com/37275728/186025613-538378ce-2cc9-4db7-9829-d513dc34a344.PNG)\n\n## Course Outline and Resources\n\nFor an enhanced learning experience, I highly recommend visiting this [complementary notes website](http://www.holehouse.org/mlclass/).\n\n| Week # | Description                             | Notes                                                                                                                 |\n|--------|-----------------------------------------|-----------------------------------------------------------------------------------------------------------------------|\n| Week 1 | Introduction to machine learning.       | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/01_introduction_to_machine_learning.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 2 | Linear Regression with One Variable.    | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/02_linear_regression.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 3 | Linear Algebra - review.                | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/03_review_of_linear_algebra.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 4 | Linear Regression with Multiple Variables.| \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/04_linear_regression_multiple_variables.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 5 | Octave                                  | -                                                                                                                     |\n| Week 6 | Logistic Regression.                    | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/06_logistic_regression.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 7 | Regularization.                        | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/07_regularization.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 8 | Neural Networks - Representation.       | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/08_neural_networks_representation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 9 | Neural Networks - Learning.             | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/09_neural_networks_learning.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 10| Advice for applying machine learning techniques.| \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/10_applying_machine_learning_advice.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 11| Machine Learning System Design.         | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/11_machine_learning_system_design.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 12| Support Vector Machines.                | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/12_support_vector_machines.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 13| Clustering.                             | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/13_clustering.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 14| Dimensionality Reduction.               | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/14_dimensionality_reduction.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 15| Anomaly Detection.                      | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/15_anomaly_detection.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 16| Recommendation Systems.                 | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/16_recommendation_systems.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 17| Large Scale Machine Learning.           | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/17_large_scale_machine_learning.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n| Week 18| Application Example - Photo OCR.        | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/slides/18_photo_ocr.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e\n\n## Programming Exercises\n\nExplore my solutions to hands-on programming exercises to solidify your understanding of the concepts taught in the course.\n\n| # | Title                                             | Solution                                                                                                          |\n|---|---------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|\n| 1 | Linear Regression.                                | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_1/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 2 | Logistic Regression.                              | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_2/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 3 | Multi-class Classification and Neural Networks.    | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_3/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 4 | Neural Network Learning.                          | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_4/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 5 | Regularized Linear Regression and Bias vs Variance.| \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_5/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 6 | Support Vector Machines.                          | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_6/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 7 | K-means Clustering and Principal Component Analysis.| \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_7/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n| 8 | Anomaly Detection and Recommendation Systems.     | \u003ca href=\"https://github.com/djeada/Stanford-Machine-Learning/blob/main/src/exercise_8/src/main.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e \n\n## How to Contribute\n\nWe encourage contributions that enhance the repository's value. To contribute:\n\n1. Fork the repository.\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`).\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).\n4. Push to the branch (`git push origin feature/AmazingFeature`).\n5. Open a Pull Request.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fstanford-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjeada%2Fstanford-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fstanford-machine-learning/lists"}